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Low-rank and joint-sparse signal recovery using sparse Bayesian learning in a WBAN

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Abstract

Wireless body area networks (WBANs) will become increasingly important in future communication systems, especially in the area of wearable health monitoring systems, such as telemonitoring systems for the collection of electrocardiogram (ECG) data/electroencephalogram (EEG) data via WBANs for e-health applications. However, wearable devices usually require limited power consumption to ensure long battery life. Fortunately, compressed sensing (CS) has been proven to use less energy than traditional transform-coding-based methods. Because the spatial and temporal data collected by a WBAN have some closely correlated structures in certain transform domains (e.g., the discrete cosine transform (DCT) domain), we exploit these structures to propose a new low-rank and joint-sparse (L&S) signal recovery algorithm for recovering ECG/EEG data in the framework of CS. Using a simultaneously L&S signal model, we employ a Bayesian learning treatment. This treatment incorporates an L&S-inducing prior over the data and appropriate hyperpriors over all hyperparameters and thereby yields an effective reconstruction of L&S data. Simulation results with synthetic and real ECG/EEG data demonstrate that the proposed algorithm is superior to other state-of-the-art recovery algorithms in terms of reconstruction performance with comparable computational complexity.

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Notes

  1. Available at https://physionet.org/physiobank/database/incartdb.

  2. Available at https://mmspg.epfl.ch/BCI_datasets.

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Acknowledgements

This work was supported in part by Beijing Natural Science Foundation-Haidian Original Innovation Foundation (L192003), in part by the National Natural Science Foundation of China under Grants 61629101 and 61871041, in part by Beijing Laboratory Funding under Grant 2019BJLAB01, and in part by the 111 Project under Grant B17007.

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Authors and Affiliations

  1. Beijing Laboratory of Advanced Information Networks, Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yan-Bin Zhang, Yang-Qing Li, Ke-Sen He, Kai Zhang & Chang-Chuan Yin

  2. School of Information Engineering, Wuhan University of Technology, Wuhan, 430205, China

    Long-Ting Huang

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  1. Yan-Bin Zhang
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  2. Long-Ting Huang
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  4. Ke-Sen He
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  5. Kai Zhang
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Correspondence to Chang-Chuan Yin.

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Zhang, YB., Huang, LT., Li, YQ. et al. Low-rank and joint-sparse signal recovery using sparse Bayesian learning in a WBAN. Multidim Syst Sign Process 32, 359–379 (2021). https://doi.org/10.1007/s11045-020-00743-y

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  • DOI: https://doi.org/10.1007/s11045-020-00743-y

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